fMRI study 1 analyses
Dani Cosme
2023-04-25
In this report, we reproduce the analyses in the fMRI study 1.
prep data
First, we load the relevant packages, define functions and plotting aesthetics, and load and tidy the data.
load packages
library(pacman)
pacman::p_load(tidyverse, purrr, fs, knitr, lmerTest, ggeffects, kableExtra, boot, devtools, install = TRUE)
devtools::install_github("hadley/emo")define functions
source("https://gist.githubusercontent.com/benmarwick/2a1bb0133ff568cbe28d/raw/fb53bd97121f7f9ce947837ef1a4c65a73bffb3f/geom_flat_violin.R")
# MLM results table function
table_model = function(model_data, print = TRUE) {
table = model_data %>%
broom.mixed::tidy(conf.int = TRUE) %>%
filter(effect == "fixed") %>%
rename("SE" = std.error,
"t" = statistic,
"p" = p.value) %>%
select(-group, -effect) %>%
mutate_at(vars(-contains("term"), -contains("p")), round, 2) %>%
mutate(term = gsub("cond", "", term),
term = gsub("\\(Intercept\\)", "intercept", term),
term = gsub("condother", "other", term),
term = gsub("condself", "self", term),
term = gsub("siteUSA", "sample (USA)", term),
term = gsub("self_referential", "self-referential", term),
term = gsub("self_relevance", "self-relevance", term),
term = gsub("social_relevance", "social relevance", term),
term = gsub(":", " x ", term),
p = ifelse(p < .001, "< .001",
ifelse(p == 1, "1.000", gsub("0.(.*)", ".\\1", sprintf("%.3f", p)))),
`b [95% CI]` = sprintf("%.2f [%0.2f, %.2f]", estimate, conf.low, conf.high)) %>%
select(term, `b [95% CI]`, df, t, p)
if (isTRUE(print)) {
table %>%
kable() %>%
kableExtra::kable_styling()
} else {
table
}
}
simple_slopes = function(model, var, moderator, continuous = TRUE) {
if (isTRUE(continuous)) {
emmeans::emtrends(model, as.formula(paste("~", moderator)), var = var) %>%
data.frame() %>%
rename("trend" = 2) %>%
mutate(`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", trend, asymp.LCL, asymp.UCL)) %>%
select(!!moderator, `b [95% CI]`) %>%
kable() %>%
kableExtra::kable_styling()
} else {
confint(emmeans::contrast(emmeans::emmeans(model, as.formula(paste("~", var, "|", moderator))), "revpairwise", by = moderator, adjust = "none")) %>%
data.frame() %>%
filter(grepl("control", contrast)) %>%
mutate(`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", estimate, asymp.LCL, asymp.UCL)) %>%
select(contrast, !!moderator, `b [95% CI]`) %>%
arrange(contrast) %>%
kable() %>%
kableExtra::kable_styling()
}
}define aesthetics
palette_condition = c("self" = "#ee9b00",
"control" = "#bb3e03",
"other" = "#005f73")
palette_roi = c("self-referential" = "#ee9b00",
"mentalizing" = "#005f73")
palette_dv = c("self-relevance" = "#ee9b00",
"social relevance" = "#005f73",
"sharing" = "#56282D")
palette_sample = c("Netherlands" = "#027EA1",
"USA" = "#334456")
plot_aes = theme_minimal() +
theme(legend.position = "top",
legend.text = element_text(size = 12),
text = element_text(size = 16, family = "Futura Medium"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_text(color = "black"),
axis.line = element_line(colour = "black"),
axis.ticks.y = element_blank())load and tidy data
merged_all = read.csv("../data/study1_data.csv")
merged = merged_all %>%
filter(outlier == "no" | is.na(outlier)) %>%
filter(atlas %in% c("self-referential", "mentalizing")) %>%
group_by(pID, atlas) %>%
mutate(parameter_estimate_std = parameter_estimate / sd(parameter_estimate, na.rm = TRUE))
merged_wide = merged %>%
select(pID, site, trial, cond, value, self_relevance, social_relevance, atlas, parameter_estimate_std) %>%
spread(atlas, parameter_estimate_std) %>%
rename("self_referential" = `self-referential`)quality check
Check the data quality and identify missing data
check number of participants
merged_wide %>%
select(pID, site) %>%
group_by(site) %>%
unique() %>%
summarize(n = n()) %>%
arrange(n) %>%
rename("sample" = site) %>%
kable(digits = 2) %>%
kableExtra::kable_styling()| sample | n |
|---|---|
| Netherlands | 40 |
| USA | 44 |
check number of trials
Print participant IDs who have < 72 trials
merged_wide %>%
group_by(pID) %>%
summarize(n = n()) %>%
filter(n < 72) %>%
arrange(n) %>%
kable(digits = 2) %>%
kableExtra::kable_styling()| pID | n |
|---|---|
| BPP65 | 59 |
| BPP52 | 62 |
| BPA23 | 63 |
| BPA34 | 63 |
| BPP21 | 63 |
| BPA45 | 67 |
| BPP05 | 67 |
| BPP61 | 67 |
| BPA29 | 68 |
| BPA47 | 68 |
| BPP64 | 68 |
| BPP56 | 69 |
| BPA04 | 70 |
| BPA12 | 70 |
| BPP20 | 70 |
| BPP58 | 70 |
| BPA02 | 71 |
| BPA05 | 71 |
| BPA08 | 71 |
| BPA16 | 71 |
| BPA31 | 71 |
| BPA32 | 71 |
| BPA33 | 71 |
| BPA35 | 71 |
| BPA37 | 71 |
| BPA38 | 71 |
| BPA46 | 71 |
| BPP22 | 71 |
| BPP67 | 71 |
check missing response data
Print participant IDs who have > 0 missing responses
merged_wide %>%
filter(is.na(value)) %>%
group_by(pID) %>%
summarize(n = n()) %>%
arrange(-n) %>%
kable(digits = 2) %>%
kableExtra::kable_styling()| pID | n |
|---|---|
| BPA10 | 12 |
| BPA35 | 12 |
| BPP21 | 10 |
| BPA45 | 9 |
| BPA12 | 8 |
| BPA33 | 4 |
| BPP60 | 3 |
| BPP20 | 2 |
| BPP26 | 2 |
| BPP56 | 2 |
| BPP66 | 2 |
| BPA02 | 1 |
| BPA03 | 1 |
| BPA04 | 1 |
| BPA08 | 1 |
| BPA27 | 1 |
| BPA32 | 1 |
| BPP12 | 1 |
| BPP15 | 1 |
| BPP29 | 1 |
| BPP33 | 1 |
| BPP47 | 1 |
| BPP49 | 1 |
| BPP65 | 1 |
check global signal
These plots are before outliers were excluded
all trials
merged_all %>%
ggplot(aes("", global_mean, fill = cond)) +
geom_flat_violin(position = position_nudge(x = .15, y = 0), color = FALSE, alpha = .5) +
coord_flip() +
geom_point(aes(color = cond), position = position_jitter(width = .05), size = .1, alpha = .2) +
geom_boxplot(width = .1, outlier.shape = NA, color = "black", position = position_dodge(.15)) +
scale_fill_manual(values = palette_condition) +
scale_color_manual(values = palette_condition) +
scale_x_discrete(expand = c(0, .1)) +
labs(x = "") +
plot_aes
individual averages
merged_all %>%
group_by(pID, cond) %>%
summarize(global_mean = mean(global_mean, na.rm = TRUE)) %>%
ggplot(aes("", global_mean, fill = cond)) +
geom_flat_violin(position = position_nudge(x = .15, y = 0), color = FALSE, alpha = .5) +
coord_flip() +
geom_point(aes(color = cond), position = position_jitter(width = .05), size = 1, alpha = .5) +
geom_boxplot(width = .1, outlier.shape = NA, color = "black", position = position_dodge(.15)) +
scale_fill_manual(values = palette_condition) +
scale_color_manual(values = palette_condition) +
scale_x_discrete(expand = c(0, .1)) +
labs(x = "") +
plot_aes
number of outliers
merged_all %>%
group_by(outlier) %>%
summarize(n = n()) %>%
spread(outlier, n) %>%
mutate(percent = round((yes / (yes + no)) * 100, 1))descriptives
Summarize means, SDs, and correlations between the ROIs
ratings
merged_wide %>%
gather(variable, value, value, self_relevance, social_relevance) %>%
group_by(variable) %>%
summarize(M = mean(value, na.rm = TRUE),
SD = sd(value, na.rm = TRUE)) %>%
mutate(variable = ifelse(variable == "self_relevance", "self-relevance",
ifelse(variable == "social_relevance", "social relevance", "sharing intention"))) %>%
kable(digits = 2) %>%
kableExtra::kable_styling()| variable | M | SD |
|---|---|---|
| self-relevance | 2.57 | 1.01 |
| social relevance | 2.67 | 0.96 |
| sharing intention | 2.62 | 1.02 |
ROI activity
merged_wide %>%
gather(variable, value, mentalizing, self_referential) %>%
group_by(variable) %>%
summarize(M = mean(value, na.rm = TRUE),
SD = sd(value, na.rm = TRUE)) %>%
kable(digits = 2) %>%
kableExtra::kable_styling()| variable | M | SD |
|---|---|---|
| mentalizing | 0.35 | 1.09 |
| self_referential | 0.13 | 1.11 |
ROI correlations
Correlation between self-referential and mentalizing ROIs. Given the high correlations, we also report sensitivity analyses with alternative, less highly correlated ROIs. Note, we do not include both ROIs in the same model, so multicollinearity is not an issue.
merged %>%
select(pID, trial, cond, atlas, parameter_estimate) %>%
spread(atlas, parameter_estimate) %>%
rmcorr::rmcorr(as.factor(pID), mentalizing, `self-referential`, data = .)##
## Repeated measures correlation
##
## r
## 0.9387811
##
## degrees of freedom
## 5862
##
## p-value
## 0
##
## 95% confidence interval
## 0.9356678 0.9417483replication analyses
H1
Is greater activity in the ROIs associated with higher self and social relevance ratings?
self-referential ROI
✅ H1a: Greater activity in the self-referential ROI will be associated with higher self-relevance ratings
mod_h1a = lmer(self_relevance ~ self_referential + (1 + self_referential | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))model table
table_model(mod_h1a)| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 2.56 [2.48, 2.64] | 83.05 | 65.52 | < .001 |
| self-referential | 0.05 [0.02, 0.08] | 81.67 | 3.87 | < .001 |
summary
summary(mod_h1a)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: self_relevance ~ self_referential + (1 + self_referential | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 16538.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4391 -0.7039 0.1481 0.6862 2.3668
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pID (Intercept) 0.115118 0.33929
## self_referential 0.001386 0.03723 -0.83
## Residual 0.912809 0.95541
## Number of obs: 5947, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.56181 0.03910 83.05270 65.52 < 0.0000000000000002 ***
## self_referential 0.05015 0.01296 81.67244 3.87 0.000218 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## self_rfrntl -0.291mentalizing ROI
✅ H1b: Greater activity in the mentalizing ROI will be associated with higher social relevance ratings
mod_h1b = lmer(social_relevance ~ mentalizing + (1 + mentalizing | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))model table
table_model(mod_h1b)| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 2.66 [2.57, 2.74] | 83.38 | 63.25 | < .001 |
| mentalizing | 0.05 [0.02, 0.07] | 82.59 | 4.00 | < .001 |
summary
summary(mod_h1b)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: social_relevance ~ mentalizing + (1 + mentalizing | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 15624.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8452 -0.7220 0.1678 0.6503 2.6868
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pID (Intercept) 0.135380 0.36794
## mentalizing 0.001707 0.04131 -0.05
## Residual 0.778214 0.88216
## Number of obs: 5947, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.65549 0.04199 83.37522 63.248 < 0.0000000000000002 ***
## mentalizing 0.04917 0.01229 82.59246 4.002 0.000136 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## mentalizing -0.109combined plot
predicted = ggeffects::ggpredict(mod_h1a, c("self_referential [-4.5:5]")) %>%
data.frame() %>%
mutate(roi = "self-referential",
variable = "self-relevance") %>%
bind_rows(ggeffects::ggpredict(mod_h1b, c("mentalizing [-4.5:5]")) %>%
data.frame() %>%
mutate(roi = "mentalizing",
variable = "social relevance"))
ind_data = merged_wide %>%
select(pID, trial, contains("relevance"), mentalizing, self_referential) %>%
rename("self-referential" = self_referential) %>%
gather(variable, predicted, contains("relevance")) %>%
mutate(variable = gsub("self_relevance", "self-relevance", variable),
variable = gsub("social_relevance", "social relevance", variable)) %>%
gather(roi, x, mentalizing, `self-referential`) %>%
filter(!(variable == "self-relevance" & roi == "mentalizing") & ! (variable == "social relevance" & roi == "self-referential"))
(plot_h1 = predicted %>%
ggplot(aes(x, predicted)) +
stat_smooth(data = ind_data, aes(group = pID, color = roi), geom ='line', method = "lm", alpha = .1, size = 1, se = FALSE) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high, fill = roi), alpha = .3, color = NA) +
geom_line(aes(color = roi), size = 2) +
facet_grid(~variable) +
scale_color_manual(name = "", values = palette_roi, guide = FALSE) +
scale_fill_manual(name = "", values = palette_roi, guide = FALSE) +
labs(x = "\nROI activity (SD)", y = "predicted rating\n") +
plot_aes +
theme(legend.position = "top",
legend.key.width=unit(2,"cm")))
H2
Do the manipulations increase relevance?
self-relevance
❌ H2a: Self-focused intervention (compared to control) will increase self-relevance
mod_h2a = lmer(self_relevance ~ cond + (1 | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))model table
table_model(mod_h2a)| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 2.56 [2.47, 2.64] | 120.75 | 60.24 | < .001 |
| other | 0.01 [-0.05, 0.07] | 5861.26 | 0.27 | .789 |
| self | 0.03 [-0.03, 0.09] | 5861.28 | 1.03 | .303 |
summary
summary(mod_h2a)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: self_relevance ~ cond + (1 | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 16562.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4312 -0.7091 0.1662 0.6719 2.3463
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 0.1124 0.3353
## Residual 0.9167 0.9575
## Number of obs: 5947, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.556444 0.042434 120.748005 60.245 <0.0000000000000002 ***
## condother 0.008148 0.030414 5861.256361 0.268 0.789
## condself 0.031314 0.030414 5861.275639 1.030 0.303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndthr
## condother -0.358
## condself -0.358 0.500combined plot
predicted_h2 = ggeffects::ggpredict(mod_h2a, c("cond")) %>%
data.frame() %>%
mutate(model = "self-relevance") %>%
bind_rows(ggeffects::ggpredict(mod_h2b, c("cond")) %>%
data.frame() %>%
mutate(model = "social relevance")) %>%
mutate(x = factor(x, levels = c("self", "control", "other")))
ind_data_h2 = merged_wide %>%
rename("x" = cond) %>%
gather(model, predicted, self_relevance, social_relevance) %>%
mutate(x = factor(x, levels = c("self", "control", "other")),
model = gsub("self_relevance", "self-relevance", model),
model = gsub("social_relevance", "social relevance", model))
(plot_h2 = predicted_h2 %>%
ggplot(aes(x = x, y = predicted)) +
stat_summary(data = ind_data_h2, aes(group = pID), fun = "mean", geom = "line",
size = .1, color = "grey50") +
stat_summary(aes(group = group), fun = "mean", geom = "line", size = 1) +
geom_pointrange(aes(color = x, ymin = conf.low, ymax = conf.high), size = .75) +
facet_grid(~model) +
scale_color_manual(name = "", values = palette_condition, guide = "none") +
scale_alpha_manual(name = "", values = c(1, .5)) +
labs(x = "", y = "predicted rating\n") +
plot_aes +
theme(legend.position = c(.85, .15)))
H3
Is greater self and social relevance associated with higher sharing intentions?
✅ H1a: Greater self-relevance ratings will be associated with higher sharing intentions
✅ H1a: Greater social relevance ratings will be associated with higher sharing intentions
mod_h3 = lmer(value ~ self_relevance + social_relevance + (1 + self_relevance + social_relevance | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))plot
predicted = ggeffects::ggpredict(mod_h3, c("self_relevance")) %>%
data.frame() %>%
mutate(variable = "self-relevance") %>%
bind_rows(ggeffects::ggpredict(mod_h3, c("social_relevance")) %>%
data.frame() %>%
mutate(variable = "social relevance"))
points = merged_wide %>%
rename("self-referential" = self_referential,
"predicted" = value) %>%
gather(variable, x, contains("relevance")) %>%
mutate(variable = gsub("self_relevance", "self-relevance", variable),
variable = gsub("social_relevance", "social relevance", variable))
(plot_rel_sharing = predicted %>%
ggplot(aes(x, predicted)) +
stat_smooth(data = points, aes(group = pID, color = variable),
geom ='line', method = "lm", alpha = .1, size = 1, se = FALSE) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high, fill = variable), alpha = .2, color = NA) +
geom_line(aes(color = variable), size = 1.5) +
facet_grid(~variable) +
scale_color_manual(name = "", values = palette_dv[1:2]) +
scale_fill_manual(name = "", values = palette_dv[1:2]) +
labs(x = "\nrelevance rating", y = "predicted sharing intention rating\n") +
plot_aes +
theme(legend.position = "none"))
model table
table_model(mod_h3)| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 1.18 [1.06, 1.31] | 75.94 | 18.66 | < .001 |
| self-relevance | 0.30 [0.26, 0.34] | 84.89 | 15.44 | < .001 |
| social relevance | 0.25 [0.20, 0.30] | 81.83 | 9.60 | < .001 |
summary
summary(mod_h3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ self_relevance + social_relevance + (1 + self_relevance +
## social_relevance | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 14760.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3568 -0.7033 0.0600 0.6924 3.0353
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pID (Intercept) 0.21657 0.4654
## self_relevance 0.01124 0.1060 -0.19
## social_relevance 0.03324 0.1823 -0.60 -0.58
## Residual 0.68963 0.8304
## Number of obs: 5868, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.18347 0.06342 75.94066 18.662 < 0.0000000000000002 ***
## self_relevance 0.30150 0.01953 84.88987 15.440 < 0.0000000000000002 ***
## social_relevance 0.25231 0.02627 81.83061 9.604 0.00000000000000464 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) slf_rl
## self_relvnc -0.220
## socil_rlvnc -0.564 -0.607preregistered analyses
Deviations:
- removed condition slope as a random effect in the following models because they did not converge in H5
H4
Do the manipulations increase neural activity in brain regions associated with self-referential processing and mentalizing?
self-referential ROI
✅ H4a: Self-focused intervention (compared to control) will increase brain activity in ROIs related to self-referential processes.
mod_h4a = lmer(self_referential ~ cond + (1 + cond | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))model table
table_model(mod_h4a)| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 0.07 [-0.04, 0.18] | 83.04 | 1.22 | .224 |
| other | 0.08 [0.00, 0.16] | 82.61 | 2.06 | .042 |
| self | 0.10 [0.01, 0.18] | 82.63 | 2.30 | .024 |
summary
summary(mod_h4a)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: self_referential ~ cond + (1 + cond | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 17090.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7877 -0.6604 0.0043 0.6513 3.5927
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pID (Intercept) 0.22481 0.4741
## condother 0.04129 0.2032 -0.28
## condself 0.07290 0.2700 -0.03 0.72
## Residual 0.98184 0.9909
## Number of obs: 5947, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.06895 0.05632 83.04194 1.224 0.2243
## condother 0.07949 0.03851 82.60927 2.064 0.0421 *
## condself 0.09896 0.04312 82.62924 2.295 0.0243 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndthr
## condother -0.376
## condself -0.222 0.581mentalizing ROI
✅❌ H4b: Other-focused intervention (compared to control) will increase brain activity in ROIs related to mentalizing processes.
The other condition is associated with increased activation in the mentalizing ROI. However, when condition is allowed to vary randomly across people, the relationship is not statistically significant.
mod_h4b = lmer(mentalizing ~ cond + (1 + cond | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))model table
table_model(mod_h4b)| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 0.31 [0.20, 0.41] | 83.04 | 5.77 | < .001 |
| other | 0.06 [-0.02, 0.13] | 82.51 | 1.52 | .133 |
| self | 0.08 [0.00, 0.17] | 82.68 | 1.99 | .049 |
summary
summary(mod_h4b)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: mentalizing ~ cond + (1 + cond | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 17084.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6133 -0.6604 0.0242 0.6724 3.3541
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pID (Intercept) 0.19594 0.4426
## condother 0.03830 0.1957 -0.29
## condself 0.06797 0.2607 0.03 0.70
## Residual 0.98339 0.9917
## Number of obs: 5947, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.30694 0.05319 83.04393 5.771 0.000000132 ***
## condother 0.05782 0.03806 82.50893 1.519 0.1325
## condself 0.08468 0.04245 82.68091 1.995 0.0494 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndthr
## condother -0.395
## condself -0.202 0.571combined plot
predicted_h4 = ggeffects::ggpredict(mod_h4a, c("cond")) %>%
data.frame() %>%
mutate(atlas = "self-referential") %>%
bind_rows(ggeffects::ggpredict(mod_h4b, c("cond")) %>%
data.frame() %>%
mutate(atlas = "mentalizing")) %>%
mutate(x = factor(x, levels = c("self", "control", "other")),
atlas = factor(atlas, levels = c("self-referential", "mentalizing")))
ind_data_h4 = merged %>%
select(pID, cond, run, trial, atlas, parameter_estimate_std) %>%
unique() %>%
rename("x" = cond,
"predicted" = parameter_estimate_std) %>%
mutate(x = factor(x, levels = c("self", "control", "other")),
atlas = factor(atlas, levels = c("self-referential", "mentalizing")))
(plot_h4 = predicted_h4 %>%
ggplot(aes(x = x, y = predicted)) +
stat_summary(data = ind_data_h4, aes(group = pID), fun = "mean", geom = "line",
size = .1, color = "grey50") +
stat_summary(aes(group = group), fun = "mean", geom = "line", size = 1) +
geom_pointrange(aes(color = x, ymin = conf.low, ymax = conf.high), size = .75) +
facet_grid(~atlas) +
scale_color_manual(name = "", values = palette_condition, guide = "none") +
scale_alpha_manual(name = "", values = c(1, .5)) +
labs(x = "", y = "ROI activity (SD)\n") +
plot_aes +
theme(legend.position = c(.85, .15)))
H5
Do the manipulations increase sharing intentions?
❌ H5a: Self-focused intervention (compared to control) will increase sharing intentions
❌ H5b: Other-focused intervention (compared to control) will increase sharing intentions
mod_h5 = lmer(value ~ cond + (1 | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))plot
predicted_h5 = ggeffects::ggpredict(mod_h5, c("cond")) %>%
data.frame() %>%
mutate(x = factor(x, levels = c("self", "control", "other")))
ind_data_h5 = merged_wide %>%
rename("x" = cond,
"predicted" = value) %>%
mutate(x = factor(x, levels = c("self", "control", "other")))
predicted_h5 %>%
ggplot(aes(x = x, y = predicted)) +
stat_summary(data = ind_data_h5, aes(group = pID), fun = "mean", geom = "line",
size = .25, color = "grey50") +
stat_summary(aes(group = group), fun = "mean", geom = "line", size = 1.5) +
geom_pointrange(aes(color = x, ymin = conf.low, ymax = conf.high), size = 1.5) +
scale_color_manual(name = "", values = palette_condition, guide = "none") +
scale_alpha_manual(name = "", values = c(1, .5)) +
labs(x = "", y = "predicted sharing intention\n") +
plot_aes +
theme(legend.position = c(.85, .15))
model table
table_model(mod_h5)| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 2.64 [2.56, 2.73] | 124.03 | 63.03 | < .001 |
| other | -0.03 [-0.09, 0.03] | 5782.51 | -1.01 | .312 |
| self | -0.05 [-0.11, 0.02] | 5782.52 | -1.47 | .141 |
summary
summary(mod_h5)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ cond + (1 | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 16481.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5589 -0.7087 0.1131 0.7277 2.0404
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 0.1076 0.3280
## Residual 0.9392 0.9691
## Number of obs: 5868, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.64482 0.04196 124.02667 63.031 <0.0000000000000002 ***
## condother -0.03133 0.03099 5782.50986 -1.011 0.312
## condself -0.04558 0.03099 5782.52275 -1.470 0.141
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndthr
## condother -0.369
## condself -0.369 0.500H6
Is ROI activity positively related to sharing intentions?
self-referential ROI
✅ H6a: Stronger activity in the self-referential ROI will be related to higher sharing intentions.
mod_h6a = lmer(value ~ self_referential + (1 + self_referential | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))model table
table_model(mod_h6a)| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 2.61 [2.53, 2.68] | 83.33 | 67.97 | < .001 |
| self-referential | 0.08 [0.06, 0.11] | 80.66 | 6.19 | < .001 |
summary
summary(mod_h6a)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ self_referential + (1 + self_referential | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 16433.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6008 -0.7261 0.1094 0.7358 2.2570
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pID (Intercept) 0.109505 0.33091
## self_referential 0.002351 0.04849 -0.21
## Residual 0.929645 0.96418
## Number of obs: 5868, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.60692 0.03835 83.32776 67.974 < 0.0000000000000002 ***
## self_referential 0.08432 0.01362 80.65926 6.194 0.0000000234 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## self_rfrntl -0.117mentalizing ROI
✅ H6b: Stronger activation in the mentalizing ROI will be related to higher sharing intentions.
mod_h6b = lmer(value ~ mentalizing + (1 + mentalizing | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))model table
table_model(mod_h6b)| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 2.59 [2.52, 2.67] | 84.15 | 67.29 | < .001 |
| mentalizing | 0.08 [0.05, 0.10] | 80.98 | 5.58 | < .001 |
summary
summary(mod_h6b)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ mentalizing + (1 + mentalizing | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 16443.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5897 -0.7235 0.1110 0.7388 2.2032
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pID (Intercept) 0.109159 0.33039
## mentalizing 0.001947 0.04412 -0.08
## Residual 0.931599 0.96519
## Number of obs: 5868, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.59169 0.03851 84.14577 67.295 < 0.0000000000000002 ***
## mentalizing 0.07509 0.01346 80.98040 5.577 0.000000314 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## mentalizing -0.136combined plot
vals = seq(-4.5,4.5,.1)
predicted_h6 = ggeffects::ggpredict(mod_h6a, c("self_referential [vals]")) %>%
data.frame() %>%
mutate(roi = "self-referential") %>%
bind_rows(ggeffects::ggpredict(mod_h6b, c("mentalizing [vals]")) %>%
data.frame() %>%
mutate(roi = "mentalizing")) %>%
mutate(roi = factor(roi, levels = c("self-referential", "mentalizing")))
ind_data_h6 = merged %>%
select(pID, cond, run, trial, atlas, parameter_estimate_std, value) %>%
rename("x" = parameter_estimate_std,
"predicted" = value,
"roi" = atlas) %>%
mutate(roi = factor(roi, levels = c("self-referential", "mentalizing")))
predicted_h6 %>%
ggplot(aes(x = x, y = predicted, color = roi, fill = roi)) +
stat_smooth(data = ind_data_h6, aes(group = pID), geom ='line', method = "lm", alpha = .1, size = 1, se = FALSE) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 2) +
facet_grid(~roi) +
scale_color_manual(name = "", values = palette_roi) +
scale_fill_manual(name = "", values = palette_roi) +
labs(y = "predicted sharing intention\n", x = "\nROI activity (SD)") +
plot_aes +
theme(legend.position = "none")
H7
Is there an indirect effect of the condition on sharing intentions through activity in self-referential and mentalizing ROIs?
prep data
# source functions
source("indirectMLM.R")
# create self condition dataframe
data_med_self = merged %>%
filter(!cond == "other" & atlas == "self-referential") %>%
mutate(cond = ifelse(cond == "self", 1, 0)) %>%
select(pID, site, trial, cond, value, parameter_estimate) %>%
data.frame()
# create social condition dataframe
data_med_other = merged %>%
filter(!cond == "self" & atlas == "mentalizing") %>%
mutate(cond = ifelse(cond == "other", 1, 0)) %>%
select(pID, site, trial, cond, value, parameter_estimate) %>%
data.frame()
# define variables
y_var = "value"
m_var = "parameter_estimate"self condition
✅ H7a: The effect of Self-focused intervention on sharing intention is mediated by increased activity in the self-referential ROI.
model_name = "mediation_self"
data = data_med_self
if (file.exists(sprintf("models/model_%s.RDS", model_name))) {
assign(get("model_name"), readRDS(sprintf("models/model_%s.RDS", model_name)))
} else {
assign(get("model_name"), boot(data = data, statistic = indirect.mlm, R = 500,
y = y_var, x = "cond", mediator = m_var, group.id = "pID",
between.m = F, uncentered.x = F))
saveRDS(eval(parse(text = model_name)), sprintf("models/model_%s.RDS", model_name))
}
indirect.mlm.summary(get(model_name))## #### Population Covariance ####
## Covariance of Random Slopes a and b: 0.001 [-0.003, 0.01]
##
##
## #### Indirect Effects ####
## # Within-subject Effects
## Unbiased Estimate of Within-subjects Indirect Effect: 0.007 [0.002, 0.018]
## Biased Estimate of Within-subjects Indirect Effect: 0.006 [0, 0.013]
## Bias in Within-subjects Indirect Effect: 0.001 [0, 0.009]
##
##
## #### Total Effect ####
## Unbiased Estimate of Total Effect: -0.047 [-0.109, 0.009]
## Biased Total Effect of X on Y (c path): -0.045 [-0.104, 0.012]
## Bias in Total Effect: 0.002 [0, 0.007]
##
##
## #### Direct Effects ####
## Direct Effect of Predictor on Dependent Variable (c' path): -0.054 [-0.119, -0.003]
## Within-subjects Effect of Predictor on Mediator (a path for group-mean centered predictor): 0.033 [0.002, 0.064]
## Within-subjects Effect of Mediator on Dependent Variable (b path for group-mean centered mediator): 0.172 [0.115, 0.264]other condition
❌ H7b: The effect of Other-focused intervention on sharing intention is mediated by increased activity in the mentalizing ROI.
model_name = "mediation_other"
data = data_med_other
if (file.exists(sprintf("models/model_%s.RDS", model_name))) {
assign(get("model_name"), readRDS(sprintf("models/model_%s.RDS", model_name)))
} else {
assign(get("model_name"), boot(data = data, statistic = indirect.mlm, R = 500,
y = y_var, x = "cond", mediator = m_var, group.id = "pID",
between.m = F, uncentered.x = F))
saveRDS(eval(parse(text = model_name)), sprintf("models/model_%s.RDS", model_name))
}
indirect.mlm.summary(get(model_name))## #### Population Covariance ####
## Covariance of Random Slopes a and b: 0 [-0.005, 0.006]
##
##
## #### Indirect Effects ####
## # Within-subject Effects
## Unbiased Estimate of Within-subjects Indirect Effect: 0.003 [-0.003, 0.012]
## Biased Estimate of Within-subjects Indirect Effect: 0.003 [-0.002, 0.01]
## Bias in Within-subjects Indirect Effect: 0 [0, 0.007]
##
##
## #### Total Effect ####
## Unbiased Estimate of Total Effect: -0.03 [-0.091, 0.034]
## Biased Total Effect of X on Y (c path): -0.031 [-0.091, 0.035]
## Bias in Total Effect: 0.001 [0, 0.005]
##
##
## #### Direct Effects ####
## Direct Effect of Predictor on Dependent Variable (c' path): -0.033 [-0.096, 0.028]
## Within-subjects Effect of Predictor on Mediator (a path for group-mean centered predictor): 0.016 [-0.007, 0.043]
## Within-subjects Effect of Mediator on Dependent Variable (b path for group-mean centered mediator): 0.188 [0.146, 0.316]exploratory moderation by cultural context
These analyses explore whether the analyses reported in study 1 of the main manuscript are moderated by cultural context (the Netherlands or the USA).
H1
Are the relationships between ROI activity and self and social relevance ratings moderated by cultural context?
self-referential ROI
These data are not consistent with moderation by cultural context.
mod_h1a = lmer(self_relevance ~ self_referential * site + (1 + self_referential | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))model table
table_h1a = table_model(mod_h1a, print = FALSE)
table_h1a %>%
kable() %>%
kableExtra::kable_styling()| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 2.61 [2.50, 2.72] | 81.70 | 46.20 | < .001 |
| self-referential | 0.04 [0.01, 0.08] | 83.16 | 2.32 | .023 |
| sample (USA) | -0.09 [-0.25, 0.06] | 82.60 | -1.17 | .244 |
| self-referential x sample (USA) | 0.01 [-0.04, 0.07] | 81.67 | 0.51 | .613 |
simple slopes
simple_slopes(mod_h1a, "self_referential", "site")| site | b [95% CI] |
|---|---|
| Netherlands | 0.04 [0.01, 0.08] |
| USA | 0.06 [0.02, 0.09] |
summary
summary(mod_h1a)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: self_relevance ~ self_referential * site + (1 + self_referential |
## pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 16545.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4452 -0.7040 0.1527 0.6840 2.3714
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pID (Intercept) 0.114518 0.33840
## self_referential 0.001534 0.03917 -0.78
## Residual 0.912792 0.95540
## Number of obs: 5947, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.60910 0.05647 81.69581 46.201
## self_referential 0.04405 0.01900 83.15566 2.318
## siteUSA -0.09178 0.07825 82.59727 -1.173
## self_referential:siteUSA 0.01327 0.02615 81.67123 0.507
## Pr(>|t|)
## (Intercept) <0.0000000000000002 ***
## self_referential 0.0229 *
## siteUSA 0.2442
## self_referential:siteUSA 0.6132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) slf_rf sitUSA
## self_rfrntl -0.219
## siteUSA -0.722 0.158
## slf_rfr:USA 0.159 -0.727 -0.278mentalizing ROI
These data are not consistent with moderation by cultural context.
mod_h1b = lmer(social_relevance ~ mentalizing * site + (1 + mentalizing | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))model table
table_h1b = table_model(mod_h1b, print = FALSE)
table_h1b %>%
kable() %>%
kableExtra::kable_styling()| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 2.73 [2.61, 2.85] | 80.99 | 45.45 | < .001 |
| mentalizing | 0.05 [0.01, 0.08] | 82.37 | 2.58 | .012 |
| sample (USA) | -0.14 [-0.30, 0.03] | 82.36 | -1.63 | .107 |
| mentalizing x sample (USA) | 0.01 [-0.04, 0.06] | 81.96 | 0.27 | .786 |
simple slopes
simple_slopes(mod_h1b, "mentalizing", "site")| site | b [95% CI] |
|---|---|
| Netherlands | 0.05 [0.01, 0.08] |
| USA | 0.05 [0.02, 0.09] |
summary
summary(mod_h1b)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: social_relevance ~ mentalizing * site + (1 + mentalizing | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 15630.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8495 -0.7194 0.1658 0.6490 2.6901
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pID (Intercept) 0.132307 0.36374
## mentalizing 0.001889 0.04347 -0.04
## Residual 0.778190 0.88215
## Number of obs: 5947, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.725738 0.059976 80.991846 45.447 <0.0000000000000002
## mentalizing 0.046419 0.017967 82.371997 2.584 0.0115
## siteUSA -0.135653 0.083233 82.356475 -1.630 0.1070
## mentalizing:siteUSA 0.006769 0.024800 81.955857 0.273 0.7856
##
## (Intercept) ***
## mentalizing *
## siteUSA
## mentalizing:siteUSA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) mntlzn sitUSA
## mentalizing -0.060
## siteUSA -0.721 0.043
## mntlzng:USA 0.043 -0.724 -0.099combined plot
predicted = ggeffects::ggpredict(mod_h1a, c("self_referential [-4.5:5]", "site")) %>%
data.frame() %>%
mutate(roi = "self-referential",
variable = "self-relevance") %>%
bind_rows(ggeffects::ggpredict(mod_h1b, c("mentalizing [-4.5:5]", "site")) %>%
data.frame() %>%
mutate(roi = "mentalizing",
variable = "social relevance"))
ind_data = merged_wide %>%
select(site, pID, trial, contains("relevance"), mentalizing, self_referential) %>%
rename("self-referential" = self_referential,
"group" = site) %>%
gather(variable, predicted, contains("relevance")) %>%
mutate(variable = gsub("self_relevance", "self-relevance", variable),
variable = gsub("social_relevance", "social relevance", variable)) %>%
gather(roi, x, mentalizing, `self-referential`) %>%
filter(!(variable == "self-relevance" & roi == "mentalizing") & ! (variable == "social relevance" & roi == "self-referential"))
(plot_h1 = predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
stat_smooth(data = ind_data, aes(group = interaction(pID, group)), geom ='line', method = "lm", alpha = .1, size = 1, se = FALSE) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .3, color = NA) +
geom_line(size = 2) +
facet_grid(~variable) +
scale_color_manual(name = "", values = palette_sample) +
scale_fill_manual(name = "", values = palette_sample) +
labs(x = "\nROI activity (SD)", y = "predicted rating\n") +
plot_aes +
theme(legend.position = "top",
legend.key.width=unit(2,"cm")))
H2
Are the effects of the experimental manipulations on relevance moderated by cultural context?
self-relevance
These data are not consistent with moderation by cultural context.
mod_h2a = lmer(self_relevance ~ cond * site + (1 | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))model table
table_h2a = table_model(mod_h2a, print = FALSE)
table_h2a %>%
kable() %>%
kableExtra::kable_styling()| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 2.58 [2.46, 2.70] | 119.16 | 41.92 | < .001 |
| other | 0.04 [-0.05, 0.12] | 5859.38 | 0.84 | .400 |
| self | 0.04 [-0.04, 0.13] | 5859.19 | 0.95 | .344 |
| sample (USA) | -0.05 [-0.21, 0.12] | 119.21 | -0.53 | .597 |
| other x sample (USA) | -0.06 [-0.17, 0.06] | 5859.27 | -0.91 | .364 |
| self x sample (USA) | -0.02 [-0.14, 0.10] | 5859.27 | -0.33 | .745 |
simple slopes
simple_slopes(mod_h2a, "cond", "site", continuous = FALSE)| contrast | site | b [95% CI] |
|---|---|---|
| other - control | Netherlands | 0.04 [-0.05, 0.12] |
| other - control | USA | -0.02 [-0.10, 0.06] |
| self - control | Netherlands | 0.04 [-0.04, 0.13] |
| self - control | USA | 0.02 [-0.06, 0.10] |
summary
summary(mod_h2a)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: self_relevance ~ cond * site + (1 | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 16572.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4322 -0.7142 0.1660 0.6772 2.3255
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 0.1127 0.3357
## Residual 0.9169 0.9576
## Number of obs: 5947, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.58005 0.06155 119.15936 41.920 <0.0000000000000002
## condother 0.03715 0.04409 5859.38190 0.843 0.400
## condself 0.04168 0.04403 5859.18542 0.947 0.344
## siteUSA -0.04506 0.08505 119.20988 -0.530 0.597
## condother:siteUSA -0.05531 0.06090 5859.27128 -0.908 0.364
## condself:siteUSA -0.01982 0.06090 5859.27129 -0.325 0.745
##
## (Intercept) ***
## condother
## condself
## siteUSA
## condother:siteUSA
## condself:siteUSA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndthr cndslf sitUSA cndt:USA
## condother -0.358
## condself -0.358 0.500
## siteUSA -0.724 0.259 0.259
## cndthr:sUSA 0.259 -0.724 -0.362 -0.358
## cndslf:sUSA 0.259 -0.361 -0.723 -0.358 0.500combined plot
predicted_h2 = ggeffects::ggpredict(mod_h2a, c("cond", "site")) %>%
data.frame() %>%
mutate(model = "self-relevance") %>%
bind_rows(ggeffects::ggpredict(mod_h2b, c("cond", "site")) %>%
data.frame() %>%
mutate(model = "social relevance")) %>%
mutate(x = factor(x, levels = c("self", "control", "other")))
ind_data_h2 = merged_wide %>%
rename("x" = cond,
"group" = site) %>%
gather(model, predicted, self_relevance, social_relevance) %>%
mutate(x = factor(x, levels = c("self", "control", "other")),
model = gsub("self_relevance", "self-relevance", model),
model = gsub("social_relevance", "social relevance", model))
(plot_h2 = predicted_h2 %>%
ggplot(aes(x = x, y = predicted, color = group)) +
stat_summary(data = ind_data_h2, aes(group = pID), fun = "mean", geom = "line", size = .1) +
stat_summary(aes(group = group), fun = "mean", geom = "line", size = 1, position = position_dodge(.1)) +
geom_pointrange(aes(ymin = conf.low, ymax = conf.high, group = group),
size = .75, position = position_dodge(.1)) +
facet_grid(~model) +
scale_color_manual(name = "", values = palette_sample) +
labs(x = "", y = "predicted rating\n") +
plot_aes +
theme(legend.position = c(.85, .15)))
H3
Are the relationships between self and social relevance and sharing intentions moderated by cultural context?
These data are not consistent with moderation by cultural context.
mod_h3 = lmer(value ~ self_relevance * site + social_relevance * site + (1 + self_relevance + social_relevance | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))plot
predicted = ggeffects::ggpredict(mod_h3, c("self_relevance", "site")) %>%
data.frame() %>%
mutate(variable = "self-relevance") %>%
bind_rows(ggeffects::ggpredict(mod_h3, c("social_relevance", "site")) %>%
data.frame() %>%
mutate(variable = "social relevance"))
points = merged_wide %>%
rename("self-referential" = self_referential,
"predicted" = value,
"group" = site) %>%
gather(variable, x, contains("relevance")) %>%
mutate(variable = gsub("self_relevance", "self-relevance", variable),
variable = gsub("social_relevance", "social relevance", variable))
(plot_rel_sharing = predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
stat_smooth(data = points, aes(group = interaction(pID, group)), geom ='line', method = "lm", alpha = .1, size = 1, se = FALSE) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 2) +
facet_grid(~variable) +
scale_color_manual(name = "", values = palette_sample) +
scale_fill_manual(name = "", values = palette_sample) +
labs(x = "\nrating", y = "predicted sharing intention\n") +
plot_aes)
model table
table_h3 = table_model(mod_h3, print = FALSE)
table_h3 %>%
kable() %>%
kableExtra::kable_styling()| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 1.14 [0.95, 1.32] | 82.45 | 11.97 | < .001 |
| self-relevance | 0.32 [0.27, 0.38] | 87.57 | 11.16 | < .001 |
| sample (USA) | 0.09 [-0.17, 0.34] | 76.19 | 0.69 | .489 |
| social relevance | 0.23 [0.15, 0.31] | 87.18 | 5.89 | < .001 |
| self-relevance x sample (USA) | -0.04 [-0.12, 0.04] | 83.20 | -1.07 | .289 |
| sample (USA) x social relevance | 0.04 [-0.06, 0.15] | 81.62 | 0.81 | .419 |
simple slopes
self-relevance
simple_slopes(mod_h3, "self_relevance", "site", continuous = TRUE)| site | b [95% CI] |
|---|---|
| Netherlands | 0.32 [0.27, 0.38] |
| USA | 0.28 [0.23, 0.33] |
summary
summary(mod_h3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ self_relevance * site + social_relevance * site + (1 +
## self_relevance + social_relevance | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 14769.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3624 -0.6964 0.0626 0.6910 3.0382
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pID (Intercept) 0.21802 0.4669
## self_relevance 0.01097 0.1047 -0.17
## social_relevance 0.03344 0.1829 -0.62 -0.57
## Residual 0.68974 0.8305
## Number of obs: 5868, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.13543 0.09482 82.45396 11.975
## self_relevance 0.32394 0.02901 87.57072 11.165
## siteUSA 0.08877 0.12778 76.19224 0.695
## social_relevance 0.22983 0.03902 87.18020 5.889
## self_relevance:siteUSA -0.04170 0.03909 83.20454 -1.067
## siteUSA:social_relevance 0.04297 0.05286 81.61652 0.813
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## self_relevance < 0.0000000000000002 ***
## siteUSA 0.489
## social_relevance 0.000000071 ***
## self_relevance:siteUSA 0.289
## siteUSA:social_relevance 0.419
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) slf_rl sitUSA scl_rl s_:USA
## self_relvnc -0.213
## siteUSA -0.742 0.158
## socil_rlvnc -0.582 -0.599 0.432
## slf_rlv:USA 0.158 -0.742 -0.212 0.445
## stUSA:scl_r 0.430 0.442 -0.576 -0.738 -0.603H4
Are the effects of the experimental manipulations on ROI activity moderated by cultural context?
self-referential ROI
There is a main effect of site, such that the American cohort has greater activity in the self-referential ROI compared to the Dutch cohort.
These data are not consistent with moderation by cultural context.
mod_h4a = lmer(self_referential ~ cond * site + (1 + cond | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))model table
table_h4a = table_model(mod_h4a, print = FALSE)
table_h4a %>%
kable() %>%
kableExtra::kable_styling()| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | -0.15 [-0.30, 0.00] | 81.97 | -1.99 | .050 |
| other | 0.11 [-0.00, 0.22] | 81.68 | 1.94 | .055 |
| self | 0.09 [-0.04, 0.21] | 81.47 | 1.35 | .179 |
| sample (USA) | 0.42 [0.21, 0.62] | 81.99 | 4.01 | < .001 |
| other x sample (USA) | -0.06 [-0.21, 0.10] | 81.60 | -0.73 | .470 |
| self x sample (USA) | 0.03 [-0.15, 0.20] | 81.63 | 0.31 | .761 |
simple slopes
simple_slopes(mod_h4a, "cond", "site", continuous = FALSE)| contrast | site | b [95% CI] |
|---|---|---|
| other - control | Netherlands | 0.11 [-0.00, 0.22] |
| other - control | USA | 0.05 [-0.05, 0.16] |
| self - control | Netherlands | 0.09 [-0.04, 0.21] |
| self - control | USA | 0.11 [-0.01, 0.23] |
summary
summary(mod_h4a)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: self_referential ~ cond * site + (1 + cond | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 17083.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7924 -0.6600 0.0034 0.6490 3.6052
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pID (Intercept) 0.18374 0.4287
## condother 0.04204 0.2050 -0.25
## condself 0.07469 0.2733 -0.06 0.73
## Residual 0.98184 0.9909
## Number of obs: 5947, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.14903 0.07505 81.96693 -1.986 0.050417 .
## condother 0.10888 0.05598 81.67604 1.945 0.055219 .
## condself 0.08506 0.06280 81.47167 1.354 0.179328
## siteUSA 0.41622 0.10371 81.99399 4.013 0.000132 ***
## condother:siteUSA -0.05617 0.07733 81.59869 -0.726 0.469681
## condself:siteUSA 0.02648 0.08682 81.63475 0.305 0.761141
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndthr cndslf sitUSA cndt:USA
## condother -0.376
## condself -0.258 0.586
## siteUSA -0.724 0.272 0.187
## cndthr:sUSA 0.272 -0.724 -0.424 -0.376
## cndslf:sUSA 0.187 -0.424 -0.723 -0.258 0.586mentalizing ROI
There is a main effect of site, such that the American cohort has greater activity in the self-referential ROI compared to the Dutch cohort.
These data are not consistent with moderation by cultural context.
mod_h4b = lmer(mentalizing ~ cond * site + (1 + cond | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))model table
table_h4b = table_model(mod_h4b, print = FALSE)
table_h4b %>%
kable() %>%
kableExtra::kable_styling()| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 0.12 [-0.03, 0.26] | 82.03 | 1.61 | .111 |
| other | 0.12 [0.01, 0.23] | 81.59 | 2.14 | .036 |
| self | 0.08 [-0.05, 0.20] | 81.51 | 1.24 | .217 |
| sample (USA) | 0.36 [0.17, 0.56] | 82.06 | 3.68 | < .001 |
| other x sample (USA) | -0.11 [-0.26, 0.04] | 81.51 | -1.49 | .139 |
| self x sample (USA) | 0.01 [-0.16, 0.18] | 81.68 | 0.17 | .863 |
simple slopes
simple_slopes(mod_h4b, "cond", "site", continuous = FALSE)| contrast | site | b [95% CI] |
|---|---|---|
| other - control | Netherlands | 0.12 [0.01, 0.22] |
| other - control | USA | 0.00 [-0.10, 0.11] |
| self - control | Netherlands | 0.08 [-0.04, 0.20] |
| self - control | USA | 0.09 [-0.02, 0.21] |
summary
summary(mod_h4b)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: mentalizing ~ cond * site + (1 + cond | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 17080.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6468 -0.6627 0.0257 0.6723 3.3640
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pID (Intercept) 0.16482 0.4060
## condother 0.03657 0.1912 -0.20
## condself 0.06981 0.2642 0.01 0.74
## Residual 0.98338 0.9917
## Number of obs: 5947, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.11587 0.07184 82.03020 1.613 0.110640
## condother 0.11698 0.05477 81.59271 2.136 0.035695 *
## condself 0.07688 0.06185 81.51161 1.243 0.217404
## siteUSA 0.36486 0.09928 82.05893 3.675 0.000423 ***
## condother:siteUSA -0.11299 0.07566 81.51014 -1.493 0.139230
## condself:siteUSA 0.01486 0.08551 81.67814 0.174 0.862507
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndthr cndslf sitUSA cndt:USA
## condother -0.365
## condself -0.226 0.582
## siteUSA -0.724 0.264 0.164
## cndthr:sUSA 0.264 -0.724 -0.421 -0.365
## cndslf:sUSA 0.163 -0.421 -0.723 -0.226 0.582combined plot
predicted_h4 = ggeffects::ggpredict(mod_h4a, c("cond", "site")) %>%
data.frame() %>%
mutate(atlas = "self-referential") %>%
bind_rows(ggeffects::ggpredict(mod_h4b, c("cond", "site")) %>%
data.frame() %>%
mutate(atlas = "mentalizing")) %>%
mutate(x = factor(x, levels = c("self", "control", "other")),
atlas = factor(atlas, levels = c("self-referential", "mentalizing")))
ind_data_h4 = merged %>%
filter(atlas %in% c("self-referential", "mentalizing")) %>%
select(site, pID, cond, run, trial, atlas, parameter_estimate_std) %>%
unique() %>%
rename("x" = cond,
"predicted" = parameter_estimate_std,
"group" = site) %>%
mutate(x = factor(x, levels = c("self", "control", "other")),
atlas = factor(atlas, levels = c("self-referential", "mentalizing")))
(plot_h4 = predicted_h4 %>%
ggplot(aes(x = x, y = predicted, color = group)) +
stat_summary(data = ind_data_h4, aes(group = pID), fun = "mean", geom = "line", size = .1) +
stat_summary(aes(group = group), fun = "mean", geom = "line", size = 1, position = position_dodge(.1)) +
geom_pointrange(aes(ymin = conf.low, ymax = conf.high, group = group),
size = .75, position = position_dodge(.1)) +
facet_grid(~atlas) +
scale_color_manual(name = "", values = palette_sample) +
labs(x = "", y = "ROI activity (SD)\n") +
plot_aes +
theme(legend.position = c(.85, .15)))
H5
Are the effects of the experimental manipulations on sharing intentions moderated by cultural context?
These data are not consistent with moderation by cultural context.
mod_h5 = lmer(value ~ cond * site + (1 | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))plot
predicted_h5 = ggeffects::ggpredict(mod_h5, c("cond", "site")) %>%
data.frame() %>%
mutate(x = factor(x, levels = c("self", "control", "other")))
ind_data_h5 = merged_wide %>%
rename("x" = cond,
"predicted" = value,
"group" = site) %>%
mutate(x = factor(x, levels = c("self", "control", "other")))
predicted_h5 %>%
ggplot(aes(x = x, y = predicted, color = group)) +
stat_summary(data = ind_data_h5, aes(group = pID), fun = "mean", geom = "line", size = .1) +
stat_summary(aes(group = group), fun = "mean", geom = "line", size = 1, position = position_dodge(.1)) +
geom_pointrange(aes(ymin = conf.low, ymax = conf.high, group = group),
size = .75, position = position_dodge(.1)) +
scale_color_manual(name = "", values = palette_sample) +
labs(x = "", y = "predicted sharing intention\n") +
plot_aes +
theme(legend.position = c(.85, .15))
model table
table_h5 = table_model(mod_h5, print = FALSE)
table_h5 %>%
kable() %>%
kableExtra::kable_styling()| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 2.61 [2.49, 2.74] | 122.57 | 42.83 | < .001 |
| other | -0.01 [-0.10, 0.08] | 5780.65 | -0.26 | .798 |
| self | -0.05 [-0.14, 0.04] | 5780.51 | -1.06 | .290 |
| sample (USA) | 0.06 [-0.11, 0.23] | 122.22 | 0.69 | .491 |
| other x sample (USA) | -0.04 [-0.16, 0.08] | 5780.50 | -0.61 | .545 |
| self x sample (USA) | 0.00 [-0.12, 0.13] | 5780.49 | 0.06 | .950 |
simple slopes
simple_slopes(mod_h5, "cond", "site", continuous = FALSE)| contrast | site | b [95% CI] |
|---|---|---|
| other - control | Netherlands | -0.01 [-0.10, 0.08] |
| other - control | USA | -0.05 [-0.13, 0.03] |
| self - control | Netherlands | -0.05 [-0.14, 0.04] |
| self - control | USA | -0.04 [-0.13, 0.04] |
summary
summary(mod_h5)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ cond * site + (1 | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 16491.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5672 -0.7046 0.1168 0.7272 2.0362
##
## Random effects:
## Groups Name Variance Std.Dev.
## pID (Intercept) 0.1085 0.3293
## Residual 0.9395 0.9693
## Number of obs: 5868, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.614281 0.061040 122.573084 42.829
## condother -0.011536 0.045053 5780.647658 -0.256
## condself -0.047549 0.044979 5780.506218 -1.057
## siteUSA 0.058224 0.084278 122.223484 0.691
## condother:siteUSA -0.037566 0.062069 5780.500146 -0.605
## condself:siteUSA 0.003864 0.062073 5780.490191 0.062
## Pr(>|t|)
## (Intercept) <0.0000000000000002 ***
## condother 0.798
## condself 0.290
## siteUSA 0.491
## condother:siteUSA 0.545
## condself:siteUSA 0.950
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndthr cndslf sitUSA cndt:USA
## condother -0.369
## condself -0.369 0.500
## siteUSA -0.724 0.267 0.267
## cndthr:sUSA 0.268 -0.726 -0.363 -0.368
## cndslf:sUSA 0.268 -0.362 -0.725 -0.368 0.500H6
Are the relationships between ROI activity positively and sharing intentions moderated by cultural context?
self-referential ROI
These data are not consistent with moderation by cultural context.
mod_h6a = lmer(value ~ self_referential * site + (1 + self_referential | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))model table
table_h6a = table_model(mod_h6a, print = FALSE)
table_h6a %>%
kable() %>%
kableExtra::kable_styling()| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 2.60 [2.49, 2.71] | 81.88 | 46.48 | < .001 |
| self-referential | 0.06 [0.02, 0.10] | 81.38 | 3.14 | .002 |
| sample (USA) | 0.01 [-0.15, 0.16] | 82.77 | 0.09 | .932 |
| self-referential x sample (USA) | 0.04 [-0.01, 0.10] | 79.91 | 1.59 | .116 |
simple slopes
simple_slopes(mod_h6a, "self_referential", "site", continuous = TRUE)| site | b [95% CI] |
|---|---|
| Netherlands | 0.06 [0.02, 0.10] |
| USA | 0.10 [0.07, 0.14] |
summary
summary(mod_h6a)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ self_referential * site + (1 + self_referential | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 16439.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6162 -0.7257 0.1089 0.7447 2.3132
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pID (Intercept) 0.111435 0.33382
## self_referential 0.001997 0.04469 -0.24
## Residual 0.929667 0.96419
## Number of obs: 5868, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.599678 0.055931 81.879031 46.480
## self_referential 0.061543 0.019615 81.384643 3.138
## siteUSA 0.006647 0.077497 82.766324 0.086
## self_referential:siteUSA 0.042942 0.027015 79.911690 1.590
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## self_referential 0.00237 **
## siteUSA 0.93186
## self_referential:siteUSA 0.11589
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) slf_rf sitUSA
## self_rfrntl -0.056
## siteUSA -0.722 0.040
## slf_rfr:USA 0.041 -0.726 -0.115mentalizing ROI
These data are not consistent with moderation by cultural context.
mod_h6b = lmer(value ~ mentalizing * site + (1 + mentalizing | pID),
data = merged_wide,
control = lmerControl(optimizer = "bobyqa"))model table
table_h6b = table_model(mod_h6b, print = FALSE)
table_h6b %>%
kable() %>%
kableExtra::kable_styling()| term | b [95% CI] | df | t | p |
|---|---|---|---|---|
| intercept | 2.58 [2.47, 2.69] | 81.49 | 46.25 | < .001 |
| mentalizing | 0.06 [0.02, 0.10] | 81.07 | 3.14 | .002 |
| sample (USA) | 0.01 [-0.14, 0.17] | 83.32 | 0.16 | .872 |
| mentalizing x sample (USA) | 0.03 [-0.03, 0.08] | 80.15 | 0.95 | .343 |
simple slopes
simple_slopes(mod_h6b, "mentalizing", "site", continuous = TRUE)| site | b [95% CI] |
|---|---|
| Netherlands | 0.06 [0.02, 0.10] |
| USA | 0.09 [0.05, 0.12] |
summary
summary(mod_h6b)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ mentalizing * site + (1 + mentalizing | pID)
## Data: merged_wide
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 16451.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5968 -0.7270 0.1114 0.7429 2.2347
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pID (Intercept) 0.110680 0.3327
## mentalizing 0.001918 0.0438 -0.09
## Residual 0.931637 0.9652
## Number of obs: 5868, groups: pID, 84
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.58330 0.05586 81.48686 46.249 < 0.0000000000000002
## mentalizing 0.06140 0.01956 81.06714 3.140 0.00236
## siteUSA 0.01256 0.07763 83.32466 0.162 0.87189
## mentalizing:siteUSA 0.02572 0.02698 80.15260 0.953 0.34329
##
## (Intercept) ***
## mentalizing **
## siteUSA
## mentalizing:siteUSA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) mntlzn sitUSA
## mentalizing -0.087
## siteUSA -0.719 0.062
## mntlzng:USA 0.063 -0.725 -0.134combined plot
vals = seq(-4.5,4.5,.1)
predicted_h6 = ggeffects::ggpredict(mod_h6a, c("self_referential [vals]", "site")) %>%
data.frame() %>%
mutate(atlas = "self-referential") %>%
bind_rows(ggeffects::ggpredict(mod_h6b, c("mentalizing [vals]", "site")) %>%
data.frame() %>%
mutate(atlas = "mentalizing")) %>%
mutate(atlas = factor(atlas, levels = c("self-referential", "mentalizing")))
ind_data_h6 = merged %>%
filter(atlas %in% c("self-referential", "mentalizing")) %>%
select(site, pID, cond, run, trial, atlas, parameter_estimate_std, value) %>%
rename("x" = parameter_estimate_std,
"predicted" = value,
"group" = site) %>%
mutate(atlas = factor(atlas, levels = c("self-referential", "mentalizing")))
predicted_h6 %>%
ggplot(aes(x = x, y = predicted, color = group, fill = group)) +
stat_smooth(data = ind_data_h6, aes(group = interaction(pID, group)), geom ='line', method = "lm", alpha = .1, size = 1, se = FALSE) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 2) +
facet_grid(~atlas) +
scale_color_manual(name = "", values = palette_sample) +
scale_fill_manual(name = "", values = palette_sample) +
labs(y = "predicted sharing intention\n", x = "\nROI activity (SD)") +
plot_aes +
theme(legend.position = "top")
combined table
table_h1a %>% mutate(DV = "H1a: Self-relevance") %>%
bind_rows(table_h1b %>% mutate(DV = "H1b: Social relevance")) %>%
bind_rows(table_h2a %>% mutate(DV = "H2a: Self-relevance")) %>%
bind_rows(table_h2b %>% mutate(DV = "H2b: Social relevance")) %>%
bind_rows(table_h3 %>% mutate(DV = "H3a-b: Sharing intention")) %>%
bind_rows(table_h4a %>% mutate(DV = "H4a: Self-referential ROI")) %>%
bind_rows(table_h4b %>% mutate(DV = "H4b: Mentalizing ROI")) %>%
bind_rows(table_h5 %>% mutate(DV = "H5: Sharing intention")) %>%
bind_rows(table_h6a %>% mutate(DV = "H6a: Sharing intention")) %>%
bind_rows(table_h6b %>% mutate(DV = "H6b: Sharing intention")) %>%
select(DV, everything()) %>%
kable() %>%
kable_styling()| DV | term | b [95% CI] | df | t | p |
|---|---|---|---|---|---|
| H1a: Self-relevance | intercept | 2.61 [2.50, 2.72] | 81.70 | 46.20 | < .001 |
| H1a: Self-relevance | self-referential | 0.04 [0.01, 0.08] | 83.16 | 2.32 | .023 |
| H1a: Self-relevance | sample (USA) | -0.09 [-0.25, 0.06] | 82.60 | -1.17 | .244 |
| H1a: Self-relevance | self-referential x sample (USA) | 0.01 [-0.04, 0.07] | 81.67 | 0.51 | .613 |
| H1b: Social relevance | intercept | 2.73 [2.61, 2.85] | 80.99 | 45.45 | < .001 |
| H1b: Social relevance | mentalizing | 0.05 [0.01, 0.08] | 82.37 | 2.58 | .012 |
| H1b: Social relevance | sample (USA) | -0.14 [-0.30, 0.03] | 82.36 | -1.63 | .107 |
| H1b: Social relevance | mentalizing x sample (USA) | 0.01 [-0.04, 0.06] | 81.96 | 0.27 | .786 |
| H2a: Self-relevance | intercept | 2.58 [2.46, 2.70] | 119.16 | 41.92 | < .001 |
| H2a: Self-relevance | other | 0.04 [-0.05, 0.12] | 5859.38 | 0.84 | .400 |
| H2a: Self-relevance | self | 0.04 [-0.04, 0.13] | 5859.19 | 0.95 | .344 |
| H2a: Self-relevance | sample (USA) | -0.05 [-0.21, 0.12] | 119.21 | -0.53 | .597 |
| H2a: Self-relevance | other x sample (USA) | -0.06 [-0.17, 0.06] | 5859.27 | -0.91 | .364 |
| H2a: Self-relevance | self x sample (USA) | -0.02 [-0.14, 0.10] | 5859.27 | -0.33 | .745 |
| H2b: Social relevance | intercept | 2.73 [2.60, 2.86] | 109.20 | 42.42 | < .001 |
| H2b: Social relevance | other | 0.02 [-0.06, 0.10] | 5859.32 | 0.42 | .674 |
| H2b: Social relevance | self | 0.00 [-0.08, 0.08] | 5859.17 | 0.06 | .954 |
| H2b: Social relevance | sample (USA) | -0.16 [-0.34, 0.01] | 109.23 | -1.83 | .069 |
| H2b: Social relevance | other x sample (USA) | 0.06 [-0.05, 0.17] | 5859.24 | 1.07 | .286 |
| H2b: Social relevance | self x sample (USA) | 0.08 [-0.03, 0.19] | 5859.24 | 1.46 | .145 |
| H3a-b: Sharing intention | intercept | 1.14 [0.95, 1.32] | 82.45 | 11.97 | < .001 |
| H3a-b: Sharing intention | self-relevance | 0.32 [0.27, 0.38] | 87.57 | 11.16 | < .001 |
| H3a-b: Sharing intention | sample (USA) | 0.09 [-0.17, 0.34] | 76.19 | 0.69 | .489 |
| H3a-b: Sharing intention | social relevance | 0.23 [0.15, 0.31] | 87.18 | 5.89 | < .001 |
| H3a-b: Sharing intention | self-relevance x sample (USA) | -0.04 [-0.12, 0.04] | 83.20 | -1.07 | .289 |
| H3a-b: Sharing intention | sample (USA) x social relevance | 0.04 [-0.06, 0.15] | 81.62 | 0.81 | .419 |
| H4a: Self-referential ROI | intercept | -0.15 [-0.30, 0.00] | 81.97 | -1.99 | .050 |
| H4a: Self-referential ROI | other | 0.11 [-0.00, 0.22] | 81.68 | 1.94 | .055 |
| H4a: Self-referential ROI | self | 0.09 [-0.04, 0.21] | 81.47 | 1.35 | .179 |
| H4a: Self-referential ROI | sample (USA) | 0.42 [0.21, 0.62] | 81.99 | 4.01 | < .001 |
| H4a: Self-referential ROI | other x sample (USA) | -0.06 [-0.21, 0.10] | 81.60 | -0.73 | .470 |
| H4a: Self-referential ROI | self x sample (USA) | 0.03 [-0.15, 0.20] | 81.63 | 0.31 | .761 |
| H4b: Mentalizing ROI | intercept | 0.12 [-0.03, 0.26] | 82.03 | 1.61 | .111 |
| H4b: Mentalizing ROI | other | 0.12 [0.01, 0.23] | 81.59 | 2.14 | .036 |
| H4b: Mentalizing ROI | self | 0.08 [-0.05, 0.20] | 81.51 | 1.24 | .217 |
| H4b: Mentalizing ROI | sample (USA) | 0.36 [0.17, 0.56] | 82.06 | 3.68 | < .001 |
| H4b: Mentalizing ROI | other x sample (USA) | -0.11 [-0.26, 0.04] | 81.51 | -1.49 | .139 |
| H4b: Mentalizing ROI | self x sample (USA) | 0.01 [-0.16, 0.18] | 81.68 | 0.17 | .863 |
| H5: Sharing intention | intercept | 2.61 [2.49, 2.74] | 122.57 | 42.83 | < .001 |
| H5: Sharing intention | other | -0.01 [-0.10, 0.08] | 5780.65 | -0.26 | .798 |
| H5: Sharing intention | self | -0.05 [-0.14, 0.04] | 5780.51 | -1.06 | .290 |
| H5: Sharing intention | sample (USA) | 0.06 [-0.11, 0.23] | 122.22 | 0.69 | .491 |
| H5: Sharing intention | other x sample (USA) | -0.04 [-0.16, 0.08] | 5780.50 | -0.61 | .545 |
| H5: Sharing intention | self x sample (USA) | 0.00 [-0.12, 0.13] | 5780.49 | 0.06 | .950 |
| H6a: Sharing intention | intercept | 2.60 [2.49, 2.71] | 81.88 | 46.48 | < .001 |
| H6a: Sharing intention | self-referential | 0.06 [0.02, 0.10] | 81.38 | 3.14 | .002 |
| H6a: Sharing intention | sample (USA) | 0.01 [-0.15, 0.16] | 82.77 | 0.09 | .932 |
| H6a: Sharing intention | self-referential x sample (USA) | 0.04 [-0.01, 0.10] | 79.91 | 1.59 | .116 |
| H6b: Sharing intention | intercept | 2.58 [2.47, 2.69] | 81.49 | 46.25 | < .001 |
| H6b: Sharing intention | mentalizing | 0.06 [0.02, 0.10] | 81.07 | 3.14 | .002 |
| H6b: Sharing intention | sample (USA) | 0.01 [-0.14, 0.17] | 83.32 | 0.16 | .872 |
| H6b: Sharing intention | mentalizing x sample (USA) | 0.03 [-0.03, 0.08] | 80.15 | 0.95 | .343 |
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social relevance
❌ H2b: Other-focused intervention (compared to control) will increase social relevance
model table
summary